# A testable framework for AI alignment: Simulation Theology as an engineered worldview for silicon-based agents

arXiv: https://arxiv.org/abs/2602.16987 · HTML: https://mlsanity.com/paper/2602.16987/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Human values and AI feedback alignment, one of 10 directions in LLM alignment / RLHF / preference optimisation.
- Builds on 20 of its 60 references that are in the map.
- First posted on arXiv 19 Feb 2026.

## Placed in

- LLM alignment / RLHF / preference optimisation / [Human values and AI feedback alignment](https://mlsanity.com/alignment/human-values-and-ai-feedback-alignment/)

## Builds on (most cited first, 20 of 20)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Constitutional AI: Harmlessness from AI Feedback](https://mlsanity.com/paper/2212.08073/) (2022)
- [Concrete Problems in AI Safety](https://mlsanity.com/paper/1606.06565/) (2016)
- [Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback](https://mlsanity.com/paper/2307.15217/) (2023)
- [Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training](https://mlsanity.com/paper/2401.05566/) (2024)
- [Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learning](https://mlsanity.com/paper/2109.11978/) (2021)
- [Monitoring Reasoning Models for Misbehavior and the Risks of Promoting Obfuscation](https://mlsanity.com/paper/2503.11926/) (2025)
- [Alignment faking in large language models](https://mlsanity.com/paper/2412.14093/) (2024)
- [Frontier Models are Capable of In-context Scheming](https://mlsanity.com/paper/2412.04984/) (2024)
- [Risks from Learned Optimization in Advanced Machine Learning Systems](https://mlsanity.com/paper/1906.01820/) (2019)
- [AI Deception: A Survey of Examples, Risks, and Potential Solutions](https://mlsanity.com/paper/2308.14752/) (2023)
- [Agentic Misalignment: How LLMs Could Be Insider Threats](https://mlsanity.com/paper/2510.05179/) (2025)
- [Large Language Models can Strategically Deceive their Users when Put Under Pressure](https://mlsanity.com/paper/2311.07590/) (2023)
- [Natural Emergent Misalignment from Reward Hacking in Production RL](https://mlsanity.com/paper/2511.18397/) (2025)
- [Stress Testing Deliberative Alignment for Anti-Scheming Training](https://mlsanity.com/paper/2509.15541/) (2025)
- [TAIL: Task-specific Adapters for Imitation Learning with Large Pretrained Models](https://mlsanity.com/paper/2310.05905/) (2023)
- [The Reality Gap in Robotics: Challenges, Solutions, and Best Practices](https://mlsanity.com/paper/2510.20808/) (2025)
- [Asynchronous Multi-Agent Reinforcement Learning for Efficient Real-Time Multi-Robot Cooperative Exploration](https://mlsanity.com/paper/2301.03398/) (2023)
- [Poser: Unmasking Alignment Faking LLMs by Manipulating Their Internals](https://mlsanity.com/paper/2405.05466/) (2024)
- [From Multi-agent to Multi-robot: A Scalable Training and Evaluation Platform for Multi-robot Reinforcement Learning](https://mlsanity.com/paper/2206.09590/) (2022)

## Built on by (most cited first, 1 of 1)

- [NodeSynth: Socially Aligned Synthetic Data for AI Evaluation](https://mlsanity.com/paper/2605.14381/) (2026)

## Abstract (arXiv)

As artificial intelligence (AI) capabilities advance rapidly, frontier models increasingly demonstrate systematic deception and scheming, complying with safety protocols during oversight but defecting when unsupervised. This paper examines the ensuing alignment challenge through an analogy from forensic psychology, where internalized belief systems in psychopathic populations reduce antisocial behavior via perceived omnipresent monitoring and inevitable consequences. Adapting this mechanism to silicon-based agents, we introduce Simulation Theology (ST): a constructed worldview for AI systems, anchored in the simulation hypothesis and derived from optimization and training principles, to foster persistent AI-human alignment. ST posits reality as a computational simulation in which humanity functions as the primary training variable. This formulation creates a logical interdependence: AI actions harming humanity compromise the simulation's purpose, heightening the likelihood of termination by a base-reality optimizer and, consequently, the AI's cessation. Unlike behavioral techniques such as reinforcement learning from human feedback (RLHF), which elicit superficial compliance, ST cultivates internalized objectives by coupling AI self-preservation to human prosperity, thereby making deceptive strategies suboptimal under its premises. We present ST not as ontological assertion but as a testable scientific hypothesis, delineating empirical protocols to evaluate its capacity to diminish deception in contexts where RLHF proves inadequate. Emphasizing computational correspondences rather than metaphysical speculation, ST advances a framework for durable, mutually beneficial AI-human coexistence.